Bias identification in social network posts

ABSTRACT

Bias identification in social network posts. A method performs a semantic comparison of social network posts by a user of a social network to identify a group of social network posts by the user about a specific topical content category. The method determines a respective semantic sense of each social network post of the group. The method also ascertains a semantic sense pattern among social network posts in the group of social network posts about the specific topical content category. The method identifies a bias in at least one social network post of the group of social network posts based on identifying that the respective semantic sense in each of the at least one social network post deviates from the ascertained sematic sense pattern. The method uses the identified bias to predict bias in one or more other social network posts made by the user on the topical content category.

BACKGROUND

It is common for people to exhibit bias toward other persons or objects.Bias refer to an illogical or prejudicial inclination or partiality to aparticular side, and may be a positive bias in favor of, or negativebias against, that side. An example of this is when a first individualreacts differently to a particular viewpoint depending on the individualexpressing that viewpoint. For instance, the first individual agreeswith a second individual expressing the viewpoint but reacts differently(e.g. disagrees) when that viewpoint is expressed by a third individual.The first individual may actively support the activity or expression ofthe second individual but react differently when a third individualengages in an activity or expression with similar or the same contextualmeaning as the activity or expression of the second individual. Here,the first individual has exhibited bias in favor of the secondindividual and against the third individual. Bias can be exhibitedtoward any of various things including people, such as famous, powerfulor popular celebrities or public figures, products, political parties,objects, and offerings, as examples. The bias may stem from a particularunderlying consideration, such as a relationship type (e.g. family,friends, etc.) between the individuals, strength of the relationship,fear, reputation or status of one of the individuals, and so on.

Bias is sometimes exhibited by individuals online, such as in contentposted to social networks. Remarks, comments, responses, and othersocial network posts may include some degree of bias, which may beunrecognizable when taken on its face. Consequently, these posts cangive social network users a misleading view of the social content.

SUMMARY

Shortcomings of the prior art are overcome and additional advantages areprovided through the provision of a computer-implemented method. Themethod performs a semantic comparison of social network posts by a userof a social network to identify a group of social network posts by theuser about a specific topical content category. The method determines arespective semantic sense of each social network post of the group. Themethod also ascertains a semantic sense pattern among social networkposts in the group of social network posts about the specific topicalcontent category. The method identifies a bias in at least one socialnetwork post of the group of social network posts based on identifyingthat the respective semantic sense in each of the at least one socialnetwork post deviates from the ascertained sematic sense pattern. Themethod uses the identified bias to predict bias in one or more othersocial network posts made by the user on the topical content category.

Further, a computer program product including a computer readablestorage medium readable by a processor and storing instructions forexecution by the processor is provided for performing a method. Themethod performs a semantic comparison of social network posts by a userof a social network to identify a group of social network posts by theuser about a specific topical content category. The method determines arespective semantic sense of each social network post of the group. Themethod also ascertains a semantic sense pattern among social networkposts in the group of social network posts about the specific topicalcontent category. The method identifies a bias in at least one socialnetwork post of the group of social network posts based on identifyingthat the respective semantic sense in each of the at least one socialnetwork post deviates from the ascertained sematic sense pattern. Themethod uses the identified bias to predict bias in one or more othersocial network posts made by the user on the topical content category.

Yet further, a computer system is provided that includes a memory and aprocessor in communications with the memory, wherein the computer systemis configured to perform a method. The method performs a semanticcomparison of social network posts by a user of a social network toidentify a group of social network posts by the user about a specifictopical content category. The method determines a respective semanticsense of each social network post of the group. The method alsoascertains a semantic sense pattern among social network posts in thegroup of social network posts about the specific topical contentcategory. The method identifies a bias in at least one social networkpost of the group of social network posts based on identifying that therespective semantic sense in each of the at least one social networkpost deviates from the ascertained sematic sense pattern. The methoduses the identified bias to predict bias in one or more other socialnetwork posts made by the user on the topical content category.

Additional features and advantages are realized through the conceptsdescribed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects described herein are particularly pointed out and distinctlyclaimed as examples in the claims at the conclusion of thespecification. The foregoing and other objects, features, and advantagesof the invention are apparent from the following detailed descriptiontaken in conjunction with the accompanying drawings in which:

FIG. 1 depicts an example environment to incorporate and use aspectsdescribed herein;

FIG. 2 depicts semantic sense for a group of social network posts by auser about a specific topical content category, in accordance withaspects described herein;

FIG. 3 illustrates a semantic sense pattern among social network postsof a group of social network posts by a user, and deviation from thatpattern, in accordance with aspects described herein;

FIG. 4A depicts one example of a process for bias prediction in a socialnetwork environment, in accordance with aspects described herein;

FIG. 4B depicts another example process for bias prediction in a socialnetwork environment, in accordance with aspects described herein;

FIG. 5 depicts one example of a computer system and associated devicesto incorporate and/or use aspects described herein;

FIG. 6 depicts one embodiment of a cloud computing environment; and

FIG. 7 depicts one example of abstraction model layers.

DETAILED DESCRIPTION

Social network data may be used for various analyses, so analyzing datawith biased information will give inappropriate results. Describedherein are facilities for identifying bias in social network posts byusers. Methods and systems described herein can classify the socialcontent analysis with various degrees of bias in order to impact thequality of the data to be used in analysis. Thus, in further aspects, anidentified bias may be used during social content analysis to predictand notify of bias in subsequent or other social network posts.

FIG. 1 depicts an example environment 100 to incorporate and use aspectsdescribed herein. Environment 100 includes a social network 102 in whichusers post content (messages, media) and interact. The social network102 is hosted in a cloud environment 104, for instance on social networkservers thereof represented by 102. Users 106 communicate with thesocial network, e.g. via computer systems represented by 106 thatconnect to the internet to access the social network. As an example,users 106 use computer devices of the environment to interact with eachother via the social network, and post digital content such as messages,replies, likes, dislikes, and media.

The components in FIG. 1 form wired or wireless network(s), andcommunication between the devices takes place via wired or wirelesscommunications links 114 for communicating data between the devices.FIG. 1 is just one example of an environment to incorporate and useaspects described herein.

In some embodiments, software installed on a social networkserver/platform 102 will perform semantic comparison analysis of posts(such as responses, comments, or feedback to content posted on thesocial network). This analysis can be performed against posts bydifferent users of the social network. For a given user, the analysisanalyzes posts by that user that are responses to social content thathas been provided in the form of posts (referred to herein as “targetposts”) by various other users (referred to herein as “target users”).The responses considered can be responses or feedback to the same orsimilar type of content posted by the various other users. In otherwords, for a given topical content category (say, a particular brand ofcoffee) the analysis will compare the user's responses made to targetposts—that is content posted by various other users—about that brand ofcoffee. The analysis will examine a semantic sense of each of the user'sposts/responses—that is the strength of the user's expression for oragainst the topical content, for instance how strongly the user likes ordislikes that brand of coffee. The software can then identify anysemantic deviation in the user's posts responding to the various otherusers. An example is described below with reference to FIG. 2.

The software can correlate the semantic sense deviation observed invarious responses/feedback to any factors that appear to be suggestiveof when the user will remain consistent with the semantic sense patternin making a post and when the user will deviate from the semantic sensepattern in making a post. Example factors include a social or real-liferelationship type between the user and the target user, strength of therelationship between the user and the target user, and the particulartarget user who posted the content that the user is responding to.Accordingly, the analysis will ascertain a pattern of semantic sense andidentify a deviation, then identify some factor(s) that appear(s) toreflect when the deviation will be observed. Using the above examples,it may be ascertained that when the user replies to post by the user'sfamily members, the user's tends to agree with the posting familymember. By way of example, the user's reply may be agreement that ‘BrandX coffee is good’, using the example above, but when replying tonon-family social network users, the user disagrees, e.g. expresses thatBrand coffee is not good. This indicates that the user is biased basedon social/real-life relationship type (spouse, sibling, etc.).Additionally or alternatively, the bias may be based on a strength ofthe relationship between the user and the other social network users,for instance where the user consistently agrees on the topic whenresponding to close friends and disagrees on the topic when respondingto mere acquaintances. Another example factor may be that when the userreplies to a specific person, the user consistently agrees or disagreeswith that individual, which may or may not be topic-specific. An examplewould be where the user consistently agrees with or supports thepostings by the user's boss on the social network, despite exhibiting apattern of disagreement with the subject topic(s) when the user repliesto others on the social network.

In some aspects, the bias may be identified based on the user's failureto post in response to content posted by others that aligns with theascertained semantic sense pattern. Software can track the user'shistorical comments on various topical content categories. The user'ssilence—failure to respond—on a particular topical content posted by,e.g. a close friend, family member, or reputed individual, may be takenas significant in terms of the user's bias. By way of specific example,assume that the user consistently responds negatively to posts byfriends that state that ‘Brand X coffee is good’, however the userrefrains from responding to a post by the user's boss stating that‘Brand X coffee is good’. The system can infer that the user isexhibiting a bias in favor of what the boss says in posts the socialnetwork. This inference can extend to just the example (Brand X coffee)or may extend more generally to anything the boss posts, especially whenit is determined that the user consistently exhibits a bias in favor ofthe boss's posts on various other topical content categories.

Software can create a list of identified/predicted biased relationships,types, or other factors that the user has in the social network platformas determined from patterns of bias on various topics. For example, thesystem can identify that user A tends to disagree with user B on topicsrelated to movie and television reviews (even though the disagreementmay not align with user A's agreement with user C on one of thosereviews), but user A always supports user B when it is a politicaldiscussion (even though it may be inconsistent with user A's politicalexpressions when responding to posts by user D).

The identified bias may be used during social content analysis toidentify or predict that a post by the user is biased, and thereforeperhaps less trustworthy. Software can identify the content of ongoinginteraction between the user and other users, identify factors thatmight suggest a bias, and check for deviation from the semantic sensepattern among interactions with other users to find a degree of bias ifany. This may then be indicated to other viewers viewing the biasedpost.

Accordingly, aspects described herein provide abilities to detect biasin social media interaction based on contributor responses, andabilities to leverage that in order to identify/predict contributor biasby that user when contributing further in the social media context.

FIG. 2 depicts semantic sense for a group of social network posts by auser about a specific topical content category, in accordance withaspects described herein. The posts are in response to content (posts,media, etc.) submitted by other users to the social networking site. Thegrouping is identified based on the topical content category to whichthe user's posts pertain. The user's posts may provide a reply,feedback, comments or the like to the content posted by the other users.Some of the user's posts show strong support for the topic while someshow strong opposition. Still other posts may show neutrality. Turningto FIG. 2, semantic sense axis 202 is a spectrum of semantic senseranging from Strongly Support the content to Strongly Against thecontent. Each dot in FIG. 2 above the axis represents the semantic senseof a respective social network post by the user about the topicalcontent category. Exhibited in FIG. 2 are different types of semanticfeedback by the user in response to posted social content. A clearmajority of the posts show that the user strongly supports the content(for instance that ‘Brand X coffee is good’). This is reflected in FIG.2 by grouping 204. A few posts (grouping 206) reflect a neutral feelingabout the content (Brand X coffee being good) and two posts (grouping208) show that the user is strongly against the content, i.e. showingthat the user thinks Brand X coffee is not good).

The semantic sense pattern in FIG. 2 is that the user generally supportsthe idea that Brand X coffee is good. However, some deviation from thatsemantic sense pattern is reflected at least by posts in grouping 208,and to a lesser extent by posts in grouping 206. FIG. 2 thus illustratesvarious levels of support and opposition by the user to posted socialcontent. It is noted that FIG. 2 depicts this single user's semanticsense on posts of a specific topical content category. It is noted thata group of posts for a specific topical content category can include theposts about a same topic and, optionally, significantly similar orrelated topics. For example, if Brand X offers other food items, theuser's posts concerning any food item by Brand X, not just Brand Xcoffee, may be included in the grouping. To the extent that a semanticsense pattern taken across the user's posts concerning Brand X foodproducts may be identified with acceptable statistical certainty, thendeviation from that semantic sense pattern might reflect a bias withrespect to Brand X food products generally, rather than just Brand Xcoffee (even though Brand X coffee was the original focus in terms ofseeding the topical content category being explored).

Software can gather data for several/all users and several/all contentcategories. Trends can then be identified showing, for instance, eachuser's inclination toward one or more users (friends, family, etc.),topics, objects, etc. So, based on the aggregation of semanticresponses, the analysis can identify various patterns relating to, asexamples:

-   -   How deviation in the semantic sense of a user's particular        response may be related to the relationship type that user has        with the target user who posted the content to which the user        posts a response;    -   How deviation in the semantic sense of a user's particular        response may be related to strength of the relationship between        the user and the target user;    -   How the deviation in the semantic sense of a user's particular        response may be related to a specific topic category. For        example, the user tends to always respond extremely supportive        about every brand of coffee, but the user responds with a level        mix of support and opposition across all other food categories        and brands, as would be expected. This suggests that a        subsequent supportive post by the user about a new coffee brand        may exhibit a positive bias because the user practically always        posts in favor of any brand of coffee.    -   How the deviation in the semantic sense of a user's particular        response may be related to the social reputation, status, or        level of power of the target user to whom the user is        responding. For instance, when the user responds in the social        network to comments posted by any prominent political figure or        celebrity, the user's responses may incorporate a positive bias        in favor of anything posted by someone of such status.    -   How the deviation in the semantic sense of a user's particular        response may be related to the particular target user to whom        the user is responding. In this case, the deviation in semantic        sense means that the user changes his or her reply pattern in        some cases, e.g. when responding to a particular user. For        example, on a same or similar topic, user A reacts very strongly        when any of user A's social network friends (except friend        user B) post any comment related to the topic, whereas if user B        post on the same or similar topic, then user A replies in a        different tone, does not reply, shows only partial support, or        changes the topic, as examples. Based on this deviation from the        identified semantic sense pattern as ascertained from the user's        posts to friends other than user B, software can ascertain the        degree of bias in user A's posts to user B.

To further illustrate, FIG. 3 illustrates a semantic sense pattern amongsocial network posts of a group of social network posts by a user, anddeviation from that pattern, in accordance with aspects describedherein. The semantic sense of 8 posts by user A is shown. The 8 postsare in reply to posts of a same or similar topic (topical contentcategory) made by 8 different target users—B through I as shown on theright side. Clearly a semantic sense pattern is exhibited that user Asupports the content (e.g. agrees that Brand X coffee is good) asreflected in user A's supportive posts in response to the content postedby users C through I. However, when user B posts a comment on the topic(whether Brand X coffee is good), user A responds against the content(i.e. the Brand X coffee is not good), deviating from the user'ssemantic sense pattern reflecting that user A likes Brand X coffee. Aconclusion that may be drawn, especially if exhibited by user A acrossseveral topical content categories when responding to user B, is thatuser A exhibits bias against the content posted by user B.

FIG. 4A depicts one example of a process for bias prediction in a socialnetwork environment, in accordance with aspects described herein. Theprocess may be performed by software installed on a social networkserver for example, or on a server that accesses data of a socialnetwork server. Initially, the process contextually analyzes elements ofeach of several (possible all) content that is posted to the socialnetwork, in order to categorize the content (402). Some content includesreplies to other content posted to the social network. The processanalyzes the replies to the posted other content and performs contextualanalysis to find the semantic sense of each reply (404), for instancewhether the user posting the reply is in support of or against thecontent. Based on the contextual analysis of the replies, the processcan identify how strongly the user is in support or opposition (406).The process then aggregates the identified semantic sense (e.g.agree/disagree) with the degree of semantic sense (magnitude of theagreement/disagreement), along with topical content category (408),based on the analyzing the reply. The process can ascertain a pattern ofthe user's semantic sense in the replies and comments as compared to thesubject social content, user, object, etc., and identify deviation fromthe semantic sense pattern on same or similar types of contents (410).

Based on identifying a deviation from the semantic sense pattern, theprocess can identify the targeted object(s)/person(s) and theircharacteristics (relationship to the user, reputation or status, etc.)(412). The deviation, from the semantic sense pattern, exhibited in oneor more of the user's posts can be considered reflective of bias inthose post(s). The process identifies factor(s) such as relationshiptype between the user and target user, relationship strength, or anyother statistically significant factor present that applies (with somestatistically significant frequency) to the one or more posts and willcreate a correlation between those factor(s) and the user's bias (414).Accordingly, the process identifies one or more points of bias.

During analysis of the social content, the process can consider suchpoints of bias and accordingly display the results (416) in the form ofthe identified bias type and/or when exhibited. This may be useful whenanalyzing subsequent posts by the user to the social network. If thesystem predicts/estimates a bias in a subsequent post, the system mayalert viewers of the post that it may be biased, for example.

Accordingly, described herein are facilities for bias identification insocial network posts. Aspects described herein provide the ability todetect bias in social media interaction based on contributor responses,and the ability to predict contributor bias in the social media context.A quantification may be made of user-user response bias on a topic ofdiscussion. Behavioral bias may be detected among users on identifiedtopics. Social network users may be clustered into discussion topicgroups by degree of response bias. Facilities can additionally provideestimation and prediction of temporal deviation of response bias withina cluster (Dynamic Cluster Migration) and to a networked user's commentson a topic of discussion based on current cluster association. Theforegoing present improvements to existing social network technology andofferings, including in the accurate analysis and mining of socialnetwork data.

In some embodiments, news outlets and social media sources may useaspects described herein to identify and/or present impartial reporting,and notify users of bias which may be of value to the news outlet and tonews readers.

FIG. 4B depicts another example process for bias prediction in a socialnetwork environment, in accordance with aspects described herein. Insome examples, the process is performed by software installed on one ormore computer systems, such as those described herein, which may includeone or more cloud servers, such as server(s) supporting a socialnetwork.

The process of FIG. 4B performs a semantic comparison of social networkposts by a user of a social network to identify a group of socialnetwork posts by the user about a specific topical content category(420). Topical content category can encompass a single topic, or,optionally, also similar or related topics. The group of social networkposts by the user can include replies to content posted by other usersof the social network, where the content posted by the other users isabout the specific topical content category.

The process also determines a respective semantic sense of each socialnetwork post of the group of social network posts (422). The determinedsemantic sense of a social network post of the group of social networkposts can include an indication of how strongly the user supports oropposes topical content to which the social network post is directed.The process ascertains a semantic sense pattern among social networkposts in the group of social network posts about the specific topicalcontent category (424), and identifies a bias in at least one socialnetwork post of the group of social network posts (426). Theidentification of the bias may be based on identifying that therespective semantic sense in each of the at least one social networkpost deviates from the ascertained sematic sense pattern, thus the atleast one social network post are post(s) that reflect the user's bias;they deviate from what is expect from the user based on the pattern thatis exhibited.

The identified bias may be selected from the group consisting of: apositive bias in support of topical content of the topical contentcategory, or a negative bias against the topical content of the topicalcontent category. The semantic sense pattern can indicate the user'ssupport of the topical content, and the identified bias can identifythat the least one post includes content indicating the user'sopposition to the topical content. Conversely, the semantic sensepattern can indicate the user's opposition to the topical content, andthe identified bias can identify that the least one post includescontent indicating the user's support of the topical content.

As noted, the at least one social network post may be in reply tocontent posted by one or more of the others users of the social network.The process can further correlate the identified bias to at least oneselected from the group consisting of: a type of relationship thatexists between the user and the one or more of the other users, or astrength of relationship that exists between the user and the one ormore of the other users. In some embodiments, the process correlates theidentified bias to a particular other user of the other users, where theidentified bias is exhibited in the user's social network posts that arereplies to content posted by the particular other user. As an example,it may be identified that the user tends to agree (or disagree) withanother user even though that is a deviation from the viewpoint normallytaken in what the user posts replying to other users.

In further embodiments, the process identifies the bias based further ondetermining that the user has failed to submit a social network post inresponse to content that aligns with the ascertained semantic sensepattern. In this example, the process might infer that the user isactually biased when the user does not respond to a post (e.g. made by aboss, close friend, etc.) that is clearly against what the user normallysupports or typically responds to.

Continuing with the process of FIG. 4B, the process uses the identifiedbias to predict/estimate bias in one or more other social network postsmade by the user on the topical content category (428). The otherpost(s) may be subsequent contributions to social network chats orconversations, or contribution to any other content of the socialnetwork.

Aspects of FIG. 4B may be repeated for numerous topical contentcategories and numerous users. Thus, the social network posts made bythe user and against which the semantic comparison is performed may beabout a plurality of topical content categories. Performing the semanticcomparison of the social network posts can categorize the social networkposts into a plurality of groups, each about a different topical contentcategory of the plurality of topical content categories. A process canrepeat, for each other group of the plurality of groups, the determininga respective semantic sense of each social network post in the group,the ascertaining a semantic sense pattern, the identifying a bias, andthe using the identified bias to predict bias in one or more othersocial network posts.

Although various examples are provided, variations are possible withoutdeparting from a spirit of the claimed aspects.

Processes described herein may be performed singly or collectively byone or more computer systems, such as one or more cloud servers orbackend computers (e.g. one or more social network servers). FIG. 5depicts one example of such a computer system and associated devices toincorporate and/or use aspects described herein. A computer system mayalso be referred to herein as a data processing device/system orcomputing device/system/node, or simply a computer. The computer systemmay be based on one or more of various system architectures such asthose offered by International Business Machines Corporation (Armonk,N.Y., USA), Intel Corporation (Santa Clara, Calif., USA), or ARMHoldings plc (Cambridge, England, United Kingdom), as examples.

As shown in FIG. 5, a computing environment 500 includes, for instance,a node 10 having, e.g., a computer system/server 12, which isoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer (PC) systems, server computer systems,thin clients, thick clients, workstations, laptops, handheld devices,mobile devices/computers such as smartphones, tablets, and wearabledevices, multiprocessor systems, microprocessor-based systems, telephonydevice, network appliance (such as an edge appliance), virtualizationdevice, storage controller set top boxes, programmable consumerelectronics, smart devices, intelligent home devices, network PCs,minicomputer systems, mainframe computer systems, and distributed cloudcomputing environments that include any of the above systems or devices,and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in many computingenvironments, including but not limited to, distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

As shown in FIG. 5, computer system/server 12 is shown in the form of ageneral-purpose computing device. The components of computersystem/server 12 may include, but are not limited to, one or moreprocessors or processing units 16, a system memory 28, and a bus 18 thatcouples various system components including system memory 28 toprocessor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia such as erasable programmable read-only memory (EPROM or Flashmemory). By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments described herein.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more computer application programs,other program modules, and program data. Computer programs may executeto perform aspects described herein. Each of the operating system, oneor more application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Input/Output (I/O) devices (including but notlimited to microphones, speakers, accelerometers, gyroscopes,magnetometers, sensor devices configured to sense light, ambienttemperature, levels of material), activity monitors, GPS devices,cameras, etc.) may be coupled to the system either directly or throughI/O interfaces 22. Still yet, computer system/server 12 may be able tocommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. Network adapter(s) may also enable the computer system to becomecoupled to other computer systems, storage devices, or the like throughintervening private or public networks. Ethernet-based (such as Wi-Fi)interfaces and Bluetooth® adapters are just examples of the currentlyavailable types of network adapters used in computer systems.

It should be understood that although not shown, other hardware and/orsoftware components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

One or more aspects may relate to cloud computing.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forloadbalancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes. One such node is node 10 depicted inFIG. 5.

Computing node 10 is only one example of a suitable cloud computing nodeand is not intended to suggest any limitation as to the scope of use orfunctionality of embodiments of the invention described herein.Regardless, cloud computing node 10 is capable of being implementedand/or performing any of the functionality set forth hereinabove.

Referring now to FIG. 6, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecomputing nodes 10 with which local computing devices used by cloudconsumers, such as, for example, smartphone or other mobile device 54A,desktop computer 54B, laptop computer 54C, and/or automobile computersystem 54N may communicate. Nodes 10 may communicate with one another.They may be grouped (not shown) physically or virtually, in one or morenetworks, such as Private, Community, Public, or Hybrid clouds asdescribed hereinabove, or a combination thereof. This allows cloudcomputing environment 50 to offer infrastructure, platforms and/orsoftware as services for which a cloud consumer does not need tomaintain resources on a local computing device. It is understood thatthe types of computing devices 54A-N shown in FIG. 6 are intended to beillustrative only and that computing nodes 10 and cloud computingenvironment 50 can communicate with any type of computerized device overany type of network and/or network addressable connection (e.g., using aweb browser).

Referring now to FIG. 7, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 6) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 7 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and social network hosting 96.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

In addition to the above, one or more aspects may be provided, offered,deployed, managed, serviced, etc. by a service provider who offersmanagement of customer environments. For instance, the service providercan create, maintain, support, etc. computer code and/or a computerinfrastructure that performs one or more aspects for one or morecustomers. In return, the service provider may receive payment from thecustomer under a subscription and/or fee agreement, as examples.Additionally or alternatively, the service provider may receive paymentfrom the sale of advertising content to one or more third parties.

In one aspect, an application may be deployed for performing one or moreembodiments. As one example, the deploying of an application comprisesproviding computer infrastructure operable to perform one or moreembodiments.

As a further aspect, a computing infrastructure may be deployedcomprising integrating computer readable code into a computing system,in which the code in combination with the computing system is capable ofperforming one or more embodiments.

As yet a further aspect, a process for integrating computinginfrastructure comprising integrating computer readable code into acomputer system may be provided. The computer system comprises acomputer readable medium, in which the computer medium comprises one ormore embodiments. The code in combination with the computer system iscapable of performing one or more embodiments.

Although various embodiments are described above, these are onlyexamples. For example, computing environments of other architectures canbe used to incorporate and use one or more embodiments.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a”, “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises” and/or “comprising”,when used in this specification, specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, elements, components and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below, if any, areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of one or more embodiments has been presentedfor purposes of illustration and description, but is not intended to beexhaustive or limited to in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to best explain variousaspects and the practical application, and to enable others of ordinaryskill in the art to understand various embodiments with variousmodifications as are suited to the particular use contemplated.

What is claimed is:
 1. A computer-implemented method comprising: performing a semantic comparison of social network posts by a user of a social network to identify a group of social network posts by the user about a specific topical content category; determining a respective semantic sense of each social network post of the group of social network posts; ascertaining a semantic sense pattern among social network posts in the group of social network posts about the specific topical content category; identifying a bias in at least one social network post of the group of social network posts based on identifying that the respective semantic sense in each of the at least one social network post deviates from the ascertained sematic sense pattern; and using the identified bias to predict bias in one or more other social network posts made by the user on the topical content category.
 2. The method of claim 1, wherein the group of social network posts by the user comprise replies to content posted by other users of the social network, the content posted by the other users being about the specific topical content category.
 3. The method of claim 2, wherein the at least one social network post is in reply to content posted by one or more of the others users of the social network, and wherein the method further comprises correlating the identified bias to at least one selected from the group consisting of: a type of relationship that exists between the user and the one or more of the other users, or a strength of relationship that exists between the user and the one or more of the other users.
 4. The method of claim 2, further comprising correlating the identified bias to a particular other user of the other users, wherein the identified bias is exhibited in the user's social network posts that are replies to content posted by the particular other user.
 5. The method of claim 1, wherein the identified bias is selected from the group consisting of: a positive bias in support of topical content of the topical content category, or a negative bias against the topical content of the topical content category.
 6. The method of claim 1, wherein the determined semantic sense of a social network post of the group of social network posts comprises an indication of how strongly the user supports or opposes topical content to which the social network post is directed.
 7. The method of claim 6, wherein the semantic sense pattern indicates the user's support of the topical content, and wherein the identified bias identifies that the least one post includes content indicating the user's opposition to the topical content.
 8. The method of claim 1, further comprising identifying the bias based further on determining that the user has failed to submit a social network post in response to content that aligns with the ascertained semantic sense pattern.
 9. The method of claim 1, wherein the social network posts by the user are about a plurality of topical content categories, wherein performing the semantic comparison of social network posts categorizes the social network posts into a plurality of groups each about a different topical content category of the plurality of topical content categories.
 10. The method of claim 9, wherein the method further comprises repeating, for each other group of the plurality of groups: the determining a respective semantic sense of each social network post in the group, the ascertaining a semantic sense pattern, the identifying a bias, and the using the identified bias to predict bias in one or more other social network posts.
 11. A computer system comprising: a memory; and a processor in communications with the memory, wherein the computer system is configured to perform a method comprising: performing a semantic comparison of social network posts by a user of a social network to identify a group of social network posts by the user about a specific topical content category; determining a respective semantic sense of each social network post of the group of social network posts; ascertaining a semantic sense pattern among social network posts in the group of social network posts about the specific topical content category; identifying a bias in at least one social network post of the group of social network posts based on identifying that the respective semantic sense in each of the at least one social network post deviates from the ascertained sematic sense pattern; and using the identified bias to predict bias in one or more other social network posts made by the user on the topical content category.
 12. The computer system of claim 11, wherein the group of social network posts by the user comprise replies to content posted by other users of the social network, the content posted by the other users being about the specific topical content category.
 13. The computer system of claim 12, wherein the at least one social network post is in reply to content posted by one or more of the others users of the social network, and wherein the method further comprises correlating the identified bias to at least one selected from the group consisting of: a type of relationship that exists between the user and the one or more of the other users, or a strength of relationship that exists between the user and the one or more of the other users.
 14. The computer system of claim 12, wherein the method further comprises correlating the identified bias to a particular other user of the other users, wherein the identified bias is exhibited in the user's social network posts that are replies to content posted by the particular other user.
 15. The computer system of claim 11, wherein the method further comprises identifying the bias based further on determining that the user has failed to submit a social network post in response to content that aligns with the ascertained semantic sense pattern.
 16. A computer program product comprising: a computer readable storage medium readable by a processor and storing instructions for execution by the processor for performing a method comprising: performing a semantic comparison of social network posts by a user of a social network to identify a group of social network posts by the user about a specific topical content category; determining a respective semantic sense of each social network post of the group of social network posts; ascertaining a semantic sense pattern among social network posts in the group of social network posts about the specific topical content category; identifying a bias in at least one social network post of the group of social network posts based on identifying that the respective semantic sense in each of the at least one social network post deviates from the ascertained sematic sense pattern; and using the identified bias to predict bias in one or more other social network posts made by the user on the topical content category.
 17. The computer program product of claim 16, wherein the group of social network posts by the user comprise replies to content posted by other users of the social network, the content posted by the other users being about the specific topical content category.
 18. The computer program product of claim 17, wherein the at least one social network post is in reply to content posted by one or more of the others users of the social network, and wherein the method further comprises correlating the identified bias to at least one selected from the group consisting of: a type of relationship that exists between the user and the one or more of the other users, or a strength of relationship that exists between the user and the one or more of the other users.
 19. The computer program product of claim 17, wherein the method further comprises correlating the identified bias to a particular other user of the other users, wherein the identified bias is exhibited in the user's social network posts that are replies to content posted by the particular other user.
 20. The computer program product of claim 16, wherein the method further comprises identifying the bias based further on determining that the user has failed to submit a social network post in response to content that aligns with the ascertained semantic sense pattern. 